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@article{174861,
        author = {Linia Mathew and Dr. Vijayalakshmi N},
        title = {Comparative analysis of YOLO Models for Real-time Personal Protective Equipment Detection(PPE)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1370-1385},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174861},
        abstract = {Personal Protective Equipment (PPE) detection is an essential aspect of maintaining workplace safety, especially in dangerous settings like building construction sites. This paper provides a comparative study of three deep learning object detection YOLOv11, YOLOv8 and YOLOv5 for PPE detection. The research utilizes a dataset from Roboflow that is composed of labelled images of protective equipment like helmets, vests and gloves. All models are compared using relevant performance indicators including accuracy, precision, recall, mean Average Precision (mAP), and inference speed. All the results clearly show that YOLOv11 performs better than its counterparts in terms of detection accuracy as well as computation efficiency, thereby making it an effective option for real-time applications of PPE monitoring. A detailed analysis is also performed based on dataset distribution, inference time, and computation needs to study the effectiveness of these models for real-time scenarios. The research identifies the need for choosing an efficient object detection model to improve labor safety and minimize occupational risks.},
        keywords = {Computer Vision, Deep Learning, Image Processing, Object Detection, Occupational Safety, PPE Detection, Real-time Monitoring, Workplace Safety, YOLOv5, YOLOv8, YOLOv11},
        month = {April},
        }